Aerodynamic parameters play a decisive role in the ballistic characteristics of the projectile. How to accurately obtain the aerodynamic parameters of the projectile is an important task in the development process of the projectile. In order to further improve the identification accuracy of the projectile drag coefficient, this paper generates huge ballistic data through numerical simulation and uses the extreme learning method to identify the ballistic drag coefficient under three kinds of noise conditions. The method avoids the iterative updating process of weights and thresholds by randomly generating the input weights and threshold values of hidden layer neurons and overcomes the problem of long identification time of the traditional back propagation (BP) neural network algorithm. Based on the least squares principle, the Moore–Penrose generalized inverse matrix of the hidden layer output matrix was solved to determine the optimal output weight of the network, and then, the projectile drag coefficient was accurately identified. Comparing the extreme learning method with the traditional BP neural network method, the results show that the proposed method has higher identification accuracy and faster convergence speed and can effectively identify the projectile drag coefficient, which can meet the practical needs of engineering.
Aiming at the problem of aerodynamic parameter identification of spinning projectile, a Cuckoo search algorithm for extreme learning machine algorithm is proposed in this paper. The algorithm uses Cuckoo search algorithm to optimize the hidden layer weight and threshold of the extreme learning machine, so as to avoid the problem of unstable identification results caused by the random weight and threshold of the traditional extreme learning machine. The simulation results show that the algorithm can effectively identify the aerodynamic parameters of projectile, with high identification accuracy and fast convergence speed. The proposed algorithm is practical in engineer applications.
Aiming at the problem of aerodynamic parameter identification of a spinning projectile, an adaptive particle swarm optimization for the extreme learning machine algorithm is proposed in this paper. The algorithm uses the adaptive particle swarm optimization algorithm to optimize the hidden layer weight and threshold of the extreme learning machine to avoid the problem of unstable identification results caused by the random weight and threshold of the traditional extreme learning machine. The free flight data of the projectile are generated by numerical simulation, and the aerodynamic parameters of a projectile are identified by the proposed algorithm. Simulation results show that the proposed algorithm can effectively identify the aerodynamic parameters of the projectile, and it has high identification accuracy and fast convergence speed. The proposed algorithm is suitable for engineering applications.
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